When 5 fingers make a person: Generalization of expertise to coarser categories

One sip, and most of us can tell with near-perfect accuracy whether the wine is red or white. And suppose it’s red, it takes relatively little expertise to know whether it’s a shiraz or a merlot. But judging whether it’s an Australian shiraz or a Chilean shiraz may require considerable expertise. Finally, being able to tell apart a Margaret River vintage from a Barossa Valley grape requires top-notch expertise and experience in wine tasting.

Across a range of domains, even slight alterations of the relevant material has been shown to lead to a reliable decrement in expert performance.

However, there has been relatively little research about another aspect of the boundaries of expertise, namely the ability to generalize to coarser categories. That is, does being able to tell whether a shiraz hails from Margaret River or the Barossa Valley enable you to classify wines along broader dimensions, such as sweetness, bouquet, or color? Does experience with classifying Picasso paintings according to period enable you to differentiate between different artists?

A recent article in the Psychonomic Bulletin & Review addressed this question using the domain of fingerprint identification as a test bed. Fingerprint examiners are highly skilled at their day-to-day task, which is to compare pairs of fingerprint impressions side-by-side to determine whether they were left by the same or different fingers.

Researchers Rachel Searston and Jason Tangen changed this task by asking experts to judge whether a series of 5 prints presented for examination belong to the same or different person. Thus, instead of comparing two prints and determining whether it belongs to the same finger, the task was to compare 5 prints and assess whether they belong to the same person. This task is not normally required of fingerprint examiners, but it involves a coarser comparison based on the experts’ acquired understanding of the variation not just between and within fingers but between and within individuals.

The figure below shows the stimuli used by Searston and Tangen. The top row shows a set of prints from the same person whereas the bottom row contains 4 prints from one person plus a print from a different person. That distractor print was always from the same hand and digit type (e.g., little finger) but from another random individual.

The participants were 23 practicing fingerprint examiners from 4 Australian police agencies with an average of 9 years experience. For comparison, Searston and Tangen also recruited a sample of 23 novice undergraduates.

Each participant was presented with 60 fingerprint “lineups” of the type shown in the above figure. Each of the 60 trials involved either a match or a mismatch, and the participant’s task was to determine whether the final print on the right belonged to the person from whom the other 4 prints were taken. In addition to making the “match-mismatch” choice, participants also indicated the confidence with which they made their choice on a 6-point scale.

The results were straightforward. Perhaps surprisingly, the novices were able to perform this task with above-chance accuracy, being able to classify nearly 69% of the lineups correctly. The prints of our 5 fingers seem to share common features that even untrained participants can pick up.

The experts, however, did quite a bit better than the novices, with nearly 76% of trials being classified correctly. A further analysis involving non-parametric statistics to disentangle true differences in discrimination ability from differences in response bias confirmed that the experts were indeed significantly more accurate than the novices but did not differ from the novices in terms of response bias.

Even though the experts were significantly more accurate than the novices, they were less confident: whereas experts rated their confidence at around 2.4 (on the 6-point scale), the novices clocked in at 3.1. The figure below summarizes both response measures:

The horizontal lines indicate the mean accuracy for each group, whereas the vertical lines denote the average confidence. Each plotting symbol represents the data from one participant.

To interpret these results it’s crucial to recall that the experts have no explicit expertise with matching people—they only match pairs of prints at the level of individual fingers. Nonetheless, the experts outperformed the novices, suggesting that the experts could transfer their acquired skill to a novel, coarser, task.

Searston and Tangen report than when experts were asked how they approached the task, they typically responded that they did not know, and that they expected their performance to be quite poor. This self-perception would explain why the confidence of the experts was lower than that of the novices.

Taken together, the results suggest that experts “develop a tacit sensitivity to the family-resemblances, covariant information, visual structure, or ‘style’ among fingerprints”, as Searston and Tangen conclude. This tacit sensitivity is not, however, accessible to explicit report by the experts themselves as they are unable to report the bases for their decisions.

In terms of practical applications, the results of Searston and Tangen suggest that expertise might not be quite as brittle as it first appears. Their results thus extend and corroborate other recent findings that we discussed earlier.

If you are an expert in Australian wines or the paintings of Picasso, your expertise may be broader than you think.